feat(algorithms): add HypE (Hypervolume Estimation)

Bader & Zitzler 2011: HypE estimates hypervolume contributions via
Monte Carlo sampling instead of computing them exactly. The point of
the trick is that exact hypervolume becomes prohibitively expensive
beyond ~5 objectives, while MC sampling stays cheap and accurate
enough at any dimension.

Each generation:
- Generate offspring via parent selection + variation + evaluation
- Combine, run non_dominated_sort, fill front-by-front
- For the splitting front, estimate each member's HV contribution
  by drawing `n_samples` uniform points in the box [ideal, reference]
  and counting how many points are dominated by *exactly* one front
  member — that count, divided by n_samples and multiplied by the
  box volume, is the member's expected unique HV contribution.
- Drop members one at a time from the splitting front by smallest
  estimated contribution.

Public API matches the rest of the MO algorithms (Config + Optimizer).
The reference point is supplied in the config so the user controls
the integration domain. Tests cover non-empty front, deterministic
reruns, and panic on dim-mismatched reference.

src/prelude.rs

@@ -24,8 +24,8 @@ pub use crate::operators::{
 pub use crate::algorithms::{
     AntColonyTsp, AntColonyTspConfig, CmaEs, CmaEsConfig, DifferentialEvolution,
     DifferentialEvolutionConfig,
-    GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Ibea,
-    IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm,
+    GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Hype,
+    HypeConfig, Ibea, IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm,
     ParticleSwarmConfig, RandomSearch, RandomSearchConfig, SimulatedAnnealing,
     SimulatedAnnealingConfig, SmsEmoa, SmsEmoaConfig, Spea2, Spea2Config, TabuSearch,
     TabuSearchConfig, Umda,